We overcome this obstacle by building on the observation that fully stable sampling paths are not required for successful defense. While the defense from [1]} uses EBMs with stable sampling for 100K steps or more, the defense results require less than 2000 steps. A natural question is whether it is possible to learn stable MCMC trajectories for only a predefined midrun range to achieve the defensive benefits without fully stabilizing samples over longrun trajectories. Defining a learning procedure to obtain such models is the goal of this section. Efficiently learning EBMs with stable midrun trajectories allows us to scale up the EBM defense to significantly more challenging domains.
